The use of an educational video on forensic autopsy in curricular teaching for medical students-is it worth the effort? : A field report from lecturers and a survey among students.

PURPOSE: The purpose of this work is to share our experience with an educational video on forensic autopsy. Using questionnaires, we attempted to answer the following questions: Does watching the video trigger emotions in students? Does the autopsy meet the expectations that they had before? Does the video help to prepare them for their subsequent autopsy participation? METHODS: A total of 365 medical students who attended their classes during the COVID-19 pandemic measures were provided with the video on an online platform. Links leading to questionnaires were positioned before and after the video. One hundred seventy-six students returned to face-to-face teaching during their course in forensic medicine. Those among them who chose to participate in an autopsy at our institute were given the link to a third questionnaire after their autopsy participation. The data was analyzed using IBM SPSS 27.0 and Microsoft Excel. RESULTS: One hundred seventy-two students completed a questionnaire before watching the educational video, 85 also completed one afterwards, and 28 completed the third questionnaire. The most intense feelings while watching the video were "curiosity" and "surprise". Out of twelve students (14.1%) who had imagined the autopsy differently in advance, five perceived the autopsy shown in the video as rougher or more brutal than expected. All autopsy participants who had previously viewed the video felt adequately prepared. CONCLUSION: Teaching should include an introduction to the handling of the corpse and the general procedures in the dissecting room. Although a video cannot substitute for personal interaction, it is useful to prepare students for their autopsy participation.

[Vasculitides]. / Vaskulitiden.

In everyday clinical practice, immunologically mediated systemic vasculitides are among the rare diseases, meaning that basic knowledge of major symptoms and indicative laboratory findings is crucial for the inclusion of these complex clinical entities in differential diagnostic considerations. For many years, systemic vasculitides have been classified according to the primarily affected vessel size, distinguishing large, medium-sized, and small vessels. Pain is very often one of the main complaints of these diseases, be it, for example, the temporally accentuated headache in giant cell arteritis, the early morning myalgias in the shoulder and hip girdle in polymyalgia rheumatica, or the mononeuritis multiplex in eosinophilic granulomatosis with polyangiitis. General symptoms such as fever, weight loss, and night sweats are often accompanied by greatly increased parameters of inflammation. In addition, organ-specific symptoms and/or laboratory abnormalities may provide crucial information. These include ENT symptoms, pulmonary or skin manifestations, as well as signs of renal involvement, such as peripheral edema, rise in blood pressure, hematuria, proteinuria, or a rapid loss of kidney function. If there is reasonable suspicion of disease, patients should be transferred to specialized centers with an interdisciplinary team. In most cases, an immunosuppressive therapy regimen is required, although in recent years the path towards avoiding high glucocorticoid doses with many side effects has been paved by the use of novel therapies.

Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates.

Previous studies showed that the neoantigen candidate load is an imperfect predictor of immune checkpoint blockade (ICB) efficacy. Further studies provided evidence that the response to ICB is also affected by the qualitative properties of a few or even single candidates, limiting the predictive power based on candidate quantity alone. Here, we predict ICB efficacy based on neoantigen candidates and their neoantigen features in the context of the mutation type, using Multiple-Instance Learning via Embedded Instance Selection (MILES). Multiple instance learning is a type of supervised machine learning that classifies labeled bags that are formed by a set of unlabeled instances. MILES performed better compared with neoantigen candidate load alone for low-abundant fusion genes in renal cell carcinoma. Our findings suggest that MILES is an appropriate method to predict the efficacy of ICB therapy based on neoantigen candidates without requiring direct T cell response information.

Discriminative machine learning for maximal representative subsampling.

Biased population samples pose a prevalent problem in the social sciences. Therefore, we present two novel methods that are based on positive-unlabeled learning to mitigate bias. Both methods leverage auxiliary information from a representative data set and train machine learning classifiers to determine the sample weights. The first method, named maximum representative subsampling (MRS), uses a classifier to iteratively remove instances, by assigning a sample weight of 0, from the biased data set until it aligns with the representative one. The second method is a variant of MRS - Soft-MRS - that iteratively adapts sample weights instead of removing samples completely. To assess the effectiveness of our approach, we induced artificial bias in a public census data set and examined the corrected estimates. We compare the performance of our methods against existing techniques, evaluating the ability of sample weights created with Soft-MRS or MRS to minimize differences and improve downstream classification tasks. Lastly, we demonstrate the applicability of the proposed methods in a real-world study of resilience research, exploring the influence of resilience on voting behavior. Through our work, we address the issue of bias in social science, amongst others, and provide a versatile methodology for bias reduction based on machine learning. Based on our experiments, we recommend to use MRS for downstream classification tasks and Soft-MRS for downstream tasks where the relative bias of the dependent variable is relevant.

Relapsing TIPIC Syndrome after Administration of an mRNA-Based COVID-19 Vaccine.

Reported vascular complications following mRNA-based COVID-19 vaccines are consisting of myocarditis, cerebral venous thrombosis, cerebral vascular thrombosis, and vaccine-induced thrombocytopenia. Here, we describe a case of a 49-year-old woman with left-sided pain above the middle common carotid artery (carotidynia) starting a few days after her second vaccination with an mRNA-based COVID-19 vaccine (Spikevax). Imaging was indicative of transient perivascular inflammation of the carotid artery (TIPIC) syndrome. The diagnostic workup for other immunologically mediated diseases was negative. The inflammation subsided after a course of prednisone and aspirin, and clinical symptoms vanished, but later mildly relapsed in the context of a viral upper respiratory tract infection other than SARS-CoV-2. Carotidynia because of TIPIC syndrome may present as an immunogenic side effect of the newly developed mRNA-based vaccinations against COVID-19. TIPIC syndrome should be considered in new-onset neck pain after vaccination.

An ultra-high-throughput screen for the evaluation of peptide HLA-Binder interactions.

Peptide human leukocyte antigen (pHLA) targeting therapeutics like T-cell receptor based adoptive cell therapy or bispecific T cell engaging receptor molecules hold great promise for the treatment of cancer. Comprehensive pre-clinical screening of therapeutic candidates is important to ensure patient safety but is challenging because of the size of the potential off-target space. By combining stabilized peptide-receptive HLA molecules with microarray printing and screening, we have developed an ultra-high-throughput screening platform named ValidaTe that enables large scale evaluation of pHLA-binder interactions. We demonstrate its potential by measuring and analyzing over 30.000 binding curves for a high-affinity T cell Engaging Receptor towards a large pHLA library. Compared to a dataset obtained by conventional bio-layer interferometry measurements, we illustrate that a massively increased throughput (over 650 fold) is obtained by our microarray screening, paving the way for use in pre-clinical safety screening of pHLA-targeting drugs.

Four-dimensional trapped ion mobility spectrometry lipidomics for high throughput clinical profiling of human blood samples.

Lipidomics encompassing automated lipid extraction, a four-dimensional (4D) feature selection strategy for confident lipid annotation as well as reproducible and cross-validated quantification can expedite clinical profiling. Here, we determine 4D descriptors (mass to charge, retention time, collision cross section, and fragmentation spectra) of 200 lipid standards and 493 lipids from reference plasma via trapped ion mobility mass spectrometry to enable the implementation of stringent criteria for lipid annotation. We use 4D lipidomics to confidently annotate 370 lipids in reference plasma samples and 364 lipids in serum samples, and reproducibly quantify 359 lipids using level-3 internal standards. We show the utility of our 4D lipidomics workflow for high-throughput applications by reliable profiling of intra-individual lipidome phenotypes in plasma, serum, whole blood, venous and finger-prick dried blood spots.

A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction.

BACKGROUND: Recent years have seen a surge of novel neural network architectures for the integration of multi-omics data for prediction. Most of the architectures include either encoders alone or encoders and decoders, i.e., autoencoders of various sorts, to transform multi-omics data into latent representations. One important parameter is the depth of integration: the point at which the latent representations are computed or merged, which can be either early, intermediate, or late. The literature on integration methods is growing steadily, however, close to nothing is known about the relative performance of these methods under fair experimental conditions and under consideration of different use cases. RESULTS: We developed a comparison framework that trains and optimizes multi-omics integration methods under equal conditions. We incorporated early integration, PCA and four recently published deep learning methods: MOLI, Super.FELT, OmiEmbed, and MOMA. Further, we devised a novel method, Omics Stacking, that combines the advantages of intermediate and late integration. Experiments were conducted on a public drug response data set with multiple omics data (somatic point mutations, somatic copy number profiles and gene expression profiles) that was obtained from cell lines, patient-derived xenografts, and patient samples. Our experiments confirmed that early integration has the lowest predictive performance. Overall, architectures that integrate triplet loss achieved the best results. Statistical differences can, overall, rarely be observed, however, in terms of the average ranks of methods, Super.FELT is consistently performing best in a cross-validation setting and Omics Stacking best in an external test set setting. CONCLUSIONS: We recommend researchers to follow fair comparison protocols, as suggested in the paper. When faced with a new data set, Super.FELT is a good option in the cross-validation setting as well as Omics Stacking in the external test set setting. Statistical significances are hardly observable, despite trends in the algorithms' rankings. Future work on refined methods for transfer learning tailored for this domain may improve the situation for external test sets. The source code of all experiments is available under https://github.com/kramerlab/Multi-Omics_analysis.

Learning to rank Higgs boson candidates.

In the extensive search for new physics, the precise measurement of the Higgs boson continues to play an important role. To this end, machine learning techniques have been recently applied to processes like the Higgs production via vector-boson fusion. In this paper, we propose to use algorithms for learning to rank, i.e., to rank events into a sorting order, first signal, then background, instead of algorithms for the classification into two classes, for this task. The fact that training is then performed on pairwise comparisons of signal and background events can effectively increase the amount of training data due to the quadratic number of possible combinations. This makes it robust to unbalanced data set scenarios and can improve the overall performance compared to pointwise models like the state-of-the-art boosted decision tree approach. In this work we compare our pairwise neural network algorithm, which is a combination of a convolutional neural network and the DirectRanker, with convolutional neural networks, multilayer perceptrons or boosted decision trees, which are commonly used algorithms in multiple Higgs production channels. Furthermore, we use so-called transfer learning techniques to improve overall performance on different data types.

Deep neural networks to recover unknown physical parameters from oscillating time series.

Deep neural networks are widely used in pattern-recognition tasks for which a human-comprehensible, quantitative description of the data-generating process, cannot be obtained. While doing so, neural networks often produce an abstract (entangled and non-interpretable) representation of the data-generating process. This may be one of the reasons why neural networks are not yet used extensively in physics-experiment signal processing: physicists generally require their analyses to yield quantitative information about the system they study. In this article we use a deep neural network to disentangle components of oscillating time series. To this aim, we design and train the neural network on synthetic oscillating time series to perform two tasks: a regression of the signal latent parameters and signal denoising by an Autoencoder-like architecture. We show that the regression and denoising performance is similar to those of least-square curve fittings with true latent-parameters initial guesses, in spite of the neural network needing no initial guesses at all. We then explore various applications in which we believe our architecture could prove useful for time-series processing, when prior knowledge is incomplete. As an example, we employ the neural network as a preprocessing tool to inform the least-square fits when initial guesses are unknown. Moreover, we show that the regression can be performed on some latent parameters, while ignoring the existence of others. Because the Autoencoder needs no prior information about the physical model, the remaining unknown latent parameters can still be captured, thus making use of partial prior knowledge, while leaving space for data exploration and discoveries.

Imaging patterns of cerebral ischemia in hypereosinophilic syndrome: case report and systematic review.

INTRODUCTION: Ischemic stroke is a potential complication of hypereosinophilic syndromes (HES), and little is known about underlying pathophysiological mechanisms. We aimed to describe the imaging patterns of cerebral ischemia in patients with HES. METHODS: An individual case is reported. A systematic PubMed review of all records reporting adult patients with HES who suffered ischemic stroke and for whom neuroimaging details of ischemic lesions were available was performed. RESULTS: A 60-year-old man presented with progressive subacute gait difficulty and psychomotor slowing as well as an absolute eosinophilia (2.2 × 109/L) at admission. Brain magnetic resonance tomography revealed multiple acute and subacute internal and external border zone infarcts. Cardiac diagnostic suggested the presence of endomyocarditis. After extensive diagnostic workup, idiopathic HES was diagnosed. The systematic review yielded 183 studies, of which 40 fulfilled the inclusion criteria: a total of 64 patients (31.3% female), with mean age 51.1 years and a median absolute eosinophile count at diagnosis of 10.2 × 109/L were included in the analyses. A border zone pattern of cerebral ischemic lesions was reported in 41 patients (64.1%). Isolated peripheral infarcts were reported in 7 patients (10.9%). Sixteen patients had multiple acute infarcts with no border zone distribution (25.0%). An intracardiac thrombus was reported in 15/60 patients (25%), and findings suggestive of endomyocarditis or endomyocardial fibrosis were found in 31/60 patients (51.7%). CONCLUSIONS: Border zone distribution of cerebral ischemia without hemodynamic compromise is the most frequent imaging pattern in patients with HES, occurring in 2/3 of patients who develop ischemic stroke.

Difficulties of Breast Reconstruction - Problems That No One Likes to Face.

Reconstructive breast surgery following total or partial mastectomy can be performed using autologous tissues or breast implants, and each has its own set of complications. Most women do not experience significant complications and are highly satisfied but breast reconstruction must consider potential complications from surgical techniques, as well as additional complications that may arise from oncological treatment modalities such as radiation therapy and chemotherapy. The aim of this article is to provide a systemic overview of possible complications that may arise in the course of reconstructive breast surgery. Complications associated with flap-based or implant-based breast reconstruction can be classified as: i) Complications inherent to surgery and common to all, including seroma, bleeding, and hematoma; skin necrosis; and infection, among others. ii) Complications specifically related to reconstruction, such as flap ischemia/necrosis/loss; fat necrosis; implant capsular contracture; implant failure, exposure, or malposition. In conclusion, this overview of possible complications is intended to improve the decision-making process when considering breast reconstruction.

Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation.

Classification approaches that allow to extract logical rules such as decision trees are often considered to be more interpretable than neural networks. Also, logical rules are comparatively easy to verify with any possible input. This is an important part in systems that aim to ensure correct operation of a given model. However, for high-dimensional input data such as images, the individual symbols, i.e. pixels, are not easily interpretable. Therefore, rule-based approaches are not typically used for this kind of high-dimensional data. We introduce the concept of first-order convolutional rules, which are logical rules that can be extracted using a convolutional neural network (CNN), and whose complexity depends on the size of the convolutional filter and not on the dimensionality of the input. Our approach is based on rule extraction from binary neural networks with stochastic local search. We show how to extract rules that are not necessarily short, but characteristic of the input, and easy to visualize. Our experiments show that the proposed approach is able to model the functionality of the neural network while at the same time producing interpretable logical rules. Thus, we demonstrate the potential of rule-based approaches for images which allows to combine advantages of neural networks and rule learning.

The dignity of the human corpse in forensic medicine.

Working with the dead is a very specific kind of work. Although a dignified handling of the corpses is demanded by the legislator and by the general public, neither the legal status of the corpse is undisputed nor is it obvious what a dignified handling of the deceased should consist of. In our hypothesis generating pilot study, we asked which concrete considerations are involved in daily practice of forensic specialists. We used an online questionnaire (invitations via e-mail) consisting of questions with single choice, multiple choice, and free text entries. The answers to single or multiple choice questions were displayed in pivot tables. The data was thus summarized, viewed, descriptively analyzed, and displayed together with the free text answers. 84.54% of the physicians and 100% of the autopsy assistants stated that considerations concerning the dignity of the deceased should play a role in daily autopsy practice. 45.87% stated that the conditions surrounding the autopsy need improvement to be ethically suitable. The analysis of the survey's results was based on Robert Audi's ethics, according to which three aspects need to be lightened in order to evaluate the conduct of a person morally: the actions, the motivation, and the way in which the actions are carried out. This systematization helps to identify the need for improvement and to make the vague demands for a dignified handling of corpses more concrete.

SARS-CoV-2 neutralizing human recombinant antibodies selected from pre-pandemic healthy donors binding at RBD-ACE2 interface.

COVID-19 is a severe acute respiratory disease caused by SARS-CoV-2, a new recently emerged sarbecovirus. This virus uses the human ACE2 enzyme as receptor for cell entry, recognizing it with the receptor binding domain (RBD) of the S1 subunit of the viral spike protein. We present the use of phage display to select anti-SARS-CoV-2 spike antibodies from the human naïve antibody gene libraries HAL9/10 and subsequent identification of 309 unique fully human antibodies against S1. 17 antibodies are binding to the RBD, showing inhibition of spike binding to cells expressing ACE2 as scFv-Fc and neutralize active SARS-CoV-2 virus infection of VeroE6 cells. The antibody STE73-2E9 is showing neutralization of active SARS-CoV-2 as IgG and is binding to the ACE2-RBD interface. Thus, universal libraries from healthy human donors offer the advantage that antibodies can be generated quickly and independent from the availability of material from recovering patients in a pandemic situation.

Effect of Core-Crosslinking on Protein Corona Formation on Polymeric Micelles.

Most nanomaterials acquire a protein corona upon contact with biological fluids. The magnitude of this effect is strongly dependent both on surface and structure of the nanoparticle. To define the contribution of the internal nanoparticle structure, protein corona formation of block copolymer micelles with poly(N-2-hydroxypropylmethacrylamide) (pHPMA) as hydrophilic shell, which are crosslinked-or not-in the hydrophobic core is comparatively analyzed. Both types of micelles are incubated with human blood plasma and separated by asymmetrical flow field-flow fractionation (AF4). Their size is determined by dynamic light scattering and proteins within the micellar fraction are characterized by gel electrophoresis and quantified by liquid chromatography-high-resolution mass spectrometry-based label-free quantitative proteomics. The analyses reveal only very low amounts of plasma proteins associated with the micelles. Notably, no significant enrichment of plasma proteins is detectable for core-crosslinked micelles, while noncrosslinked micelles show a significant enrichment of plasma proteins, indicative of protein corona formation. The results indicate that preventing the reorganization of micelles (equilibrium with unimers) by core-crosslinking is crucial to reduce the interaction with plasma proteins.

Identifying volatile organic compounds used for olfactory navigation by homing pigeons.

Many bird species have the ability to navigate home after being brought to a remote, even unfamiliar location. Environmental odours have been demonstrated to be critical to homeward navigation in over 40 years of experiments, yet the chemical identity of the odours has remained unknown. In this study, we investigate potential chemical navigational cues by measuring volatile organic compounds (VOCs): at the birds' home-loft; in selected regional forest environments; and from an aircraft at 180 m. The measurements showed clear regional, horizontal and vertical spatial gradients that can form the basis of an olfactory map for marine emissions (dimethyl sulphide, DMS), biogenic compounds (terpenoids) and anthropogenic mixed air (aromatic compounds), and temporal changes consistent with a sea-breeze system. Air masses trajectories are used to examine GPS tracks from released birds, suggesting that local DMS concentrations alter their flight directions in predictable ways. This dataset reveals multiple regional-scale real-world chemical gradients that can form the basis of an olfactory map suitable for homing pigeons.

Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia.

Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are applied to construct an index which predicts responsiveness in anesthetized patients. The present analysis considers several classification algorithms, among those support vector machines, artificial neural networks and Bayesian learning algorithms. On the basis of data from the transition between consciousness and unconsciousness, a combination of EEG and AEP signal parameters developed with automated methods provides a maximum prediction probability of 0.935, which is higher than 0.916 (for EEG parameters) and 0.880 (for AEP parameters) using a cross-validation approach. This suggests that machine learning techniques can successfully be applied to develop an improved combined EEG and AEP parameter to separate consciousness from unconsciousness.